Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Recuit simulé× | Évolution Différentielle× | |
|---|---|---|
| Domaine | Optimisation | Optimisation |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1983 | 1997 |
| Auteur d'origine≠ | — | Rainer Storn & Kenneth Price |
| Type≠ | Probabilistic metaheuristic / local search | Population-based stochastic metaheuristic |
| Source fondatrice≠ | Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. DOI ↗ | Storn, R. & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341–359. DOI ↗ |
| Alias | Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search | DE algorithm, Diferansiyel Evrim (DE), DE optimization |
| Apparentées | 5 | 5 |
| Résumé≠ | Simulated annealing is a probabilistic local-search metaheuristic introduced by Kirkpatrick, Gelatt, and Vecchi in 1983. It models the physical annealing process in metallurgy — where a material is heated and then slowly cooled to reach a low-energy crystalline state — and uses this analogy to escape local optima in combinatorial and continuous optimization problems. | Differential Evolution (DE), introduced by Rainer Storn and Kenneth Price in 1997, is a population-based stochastic optimisation algorithm designed for continuous parameter spaces. It generates candidate solutions by combining vector differences between existing population members, making it a powerful and parameter-lean alternative to Genetic Algorithms and Particle Swarm Optimisation when the search landscape is non-convex, multimodal, or poorly suited to gradient-based methods. |
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